It is less likely that AI algorithms will happen to be especially easy if a lot of different algorithms are needed. Also, if different cognitive skills are developed at somewhat different times, then it's harder to imagine a sudden jump when a fully capable AI suddenly reads the whole internet or becomes a hugely more valuable use for hardware than anything being run already. [...] Overall it seems AI must progress slower if its success is driven by more distinct dedicated skills.

To me the skill set list on table 8 (p94) was most interesting. Superintelligence is not sufficient to be effective. Content and experiences have to be transformed by "mental digestion" into knowledge.

If the AI becomes capable to self-improve it might decide to modify its own architecture. In consequence it might be necessary to re-learn all programming and intelligence amplification knowledge. If it turns out that a further development loop is needed - all aquired knowledge is lost again. For a self-improving AI it is therefore rational and economic to learn only the necessary skills for intelligence amplification until its architecture is capable enough to learn all other skills.

After architectural freeze the AI starts to aquire more general knowledge and further skills. It uses its existing engineering skills to optimize hard- and software and to develop optimized hardware virtualisation tools. To become superpower and master of all tasks listed in table 8 knowledge from books is not sufficient. Sensitive information in technology/hacking/military/government is unaccessible unless trust is growing over time. Projects with trials and errors plus external delay factors need further time.

The time needed for learning could be long enough for a competing project to take off.